FATE-LLM is a framework designed for federated learning (FL) of large and small language models (LLMs/SLMs). It targets researchers and practitioners aiming to train LLMs on distributed, private data without centralizing it. The framework enhances training efficiency through parameter-efficient methods and protects intellectual property and data privacy during training and inference.

How It Works

FATE-LLM leverages federated learning principles to enable collaborative model training across multiple clients holding private data. Its core design emphasizes parameter-efficient training techniques to reduce communication overhead and computational cost, making FL for LLMs more feasible. It incorporates privacy-preserving mechanisms to safeguard sensitive data and protect model IP, such as FedIPR.

Quick Start & Requirements

FATE-LLM supports both standalone and cluster deployments. Standalone deployment requires FATE (v2.2.0+ for latest features, specific versions for older releases) and FATE-Flow. Installation involves deploying FATE and then either installing FATE-LLM via pip and using its Launcher, or deploying FATE, FATE-Flow, and FATE-Client from PyPI for pipeline-based task execution. Lower versions require manual cloning and PYTHONPATH configuration. Cluster deployment utilizes provided packages. Detailed deployment tutorials are available.

Highlighted Details

• Supports various federated LLM training paradigms including FedMKT (Federated Mutual Knowledge Transfer), FedCoT (Federated Chain-of-Thought Distillation), PPC-GPT (Pruning and Chain-of-Thought Distillation), FDKT (Federated Domain-Specific Knowledge Transfer), Offsite Tuning, FedKSeed (Federated Full-Parameter Tuning), and InferDPT (Privacy-preserving Inference).
• Focuses on parameter-efficient methods to improve training efficiency and reduce communication costs.
• Includes mechanisms for IP protection (FedIPR) and data privacy during training and inference.
• Offers a Python SDK and CLI for usage and evaluation.

Maintenance & Community

No specific details regarding active contributors, community channels (like Discord/Slack), sponsorships, or a public roadmap were found in the provided text.

Licensing & Compatibility

The license type and any compatibility notes for commercial use or closed-source linking are not explicitly stated in the provided README content.

Limitations & Caveats

Deployment instructions vary significantly based on the FATE-LLM version, potentially leading to setup complexity. Specific hardware requirements, such as GPU or CUDA versions, are not detailed in the provided text. The absence of explicit licensing information poses a potential adoption blocker for commercial applications.